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Enhancing Multi-source Localization via Tailored Feature Representation Framework
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AI
DOI：10.1007/978-981-95-3055-7_19.png)
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Abstract
En 中文
Fast and accurate source localization can minimize the harm caused by rumors. However, due to the diversity and complexity of the dissemination of information, identifying the source of rumors on social networks remains a crucial and unresolved task. Meanwhile, the low-dimensional label features of existing methods limit the expressiveness of node representations. In the paper, we propose an Enhancing Multi-Source Localization via Tailored Feature Representation Framework (SL-TFRF) to address this limitation. Specifically, we design a feature representation module that utilizes embedding layers and contrastive learning to expand the dimensionality of node features. Furthermore, we introduce a novel attention fusion method inspired by the sliding windows to account for the varying information transmission efficiencies of different nodes. In addition, we develop a class balancing mechanism to alleviate the label imbalance inherent in source localization. Extensive experiments validate the effectiveness of SL-TFRF and demonstrate its superiority over state-of-the-art methods.
Keywords:
Social Network
Information Dissemination
Source Localization
Class Imbalance
Journal
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Papers:
28
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Cited Papers
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A Heuristic Framework for Sources Detection in Social Networks via Graph Convolutional Networks
PREAI
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